Well, we are sorry - but sometimes we can learn more from surprising negative results.
We solicit contributions which explicitly formulate a hypothesis related to deep learning or its applications (based on first principles or prior work), and then empirically falsify it through experiments. We further encourage submissions to go a layer deeper and investigate the causes of an initial idea not working as expected. This workshop will showcase how negative results offer important learning opportunities for deep learning researchers, possibly far greater than the incremental improvements found in conventional machine learning papers!
Paper Submission Deadline - Sep 30, 2022
Paper Acceptance Notification - Oct 20, 2022
In-Person Workshop - December 3, 2022